Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications on 05/15/2026
Claims 2, 6, 8, 9, 14, and 16-22 amended
Claims 1, 4, 5, and 11-13 canceled
Claims 24 and 25 new
Claims 3, 7, 10, 15, and 23 original
Claims 2-3, 6-10, 14-25 pending
Claims 2-3, 6-10, 14-25 rejected
Final Action
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Information Disclosure Statement
Responsive to IDS received on 02/26/2026. All references considered except where lined through. IDS accepted by the examiner.
Response to Arguments
Response to Objections
Claims 2, 12, 14, and 18 were objected to. Applicant has amended the claims to overcome the previous objections. Examiner confirms applicant amendments overcome the previous claim objections. Examiner has withdrawn the claim objections.
Drawings were objected to; applicant has provided replacement sheets for figures 3 and 4 without adding new matter. Figures 3 and 4 have been amended to include (302) and (304) as well as removing (408B) and (408C). Specifications has been amended to remove reference to (502). Therefore drawings objections have been overcome and the examiner withdraws the objections to the drawings.
Response to 112 Rejections
Claims 2, 3, 14, and 15 were previously rejected under 112(a) for failing to comply with the written description requirement. Applicant has amended the claims to overcome the rejection. Specifically newly added claim limitation 24 has been amended so that “additional information” can include “historical placement of a local best,” where the specification states Par 76 states: “A machine learning model can be developed (e.g., trained) to retrieve historical particle swarm algorithm data for training (e.g., through supervised learning). As a result, the machine learning can be used to optimize performance (e.g., through predicting accurate sizing requirements and reconfiguring resource allocations accordingly). Therefore examiner has withdrawn the previous 112(a) rejection.
Claims 6, 7, 19, and 20 were previously rejected under 112(b) for being indefinite. Applicant has amended the claims to overcome the rejection. Specifically, claim 24 has been amended to include “shared information.” Therefore examiner has withdrawn the previous 112(b) rejection.
Response to 101 Rejection
Claims 1-23 were previously rejected under 101 for being directed to non-statutory subject matter. Applicant has written new independent claims 24 and 25 which they argue overcome the 101 rejection.
1.Issue: Applicant argues that invention as currently claimed does not merely use conventional technology and is not abstract. Applicant argues that this is because a specific computer-implemented technique is recited for "optimizing particle swarm algorithm" execution, including the steps of the claim as recited. The applicant argues that the above limitations are concrete algorithmic controls, not abstract results, and improve a computer-implemented optimization process itself, for example, by "reducing premature convergence and optimizing execution of the particle swarm algorithm."
Rule: MPEP 2106.05(a)(I) states “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691”
MPEP 2106.05(b) “It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750,”
MPEP 2106.05(a)(II) states “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology”
Analysis: the examiner disagrees with the applicants the assertion that the claimed invention does not merely use conventional technology. The claims are directed to “a Computer implemented Method” without any further recitation of a structure which improves the computers functionality itself. Rather, the claim is towards a particle swarm algorithm (which is a mathematic algorithm) which uses a computer to perform that function. There is not specific data structures in the claim which improves the way a computer a computer performs the mathematic operations.
The examiner disagrees with the assertion that the claims represent a specific computer implemented technique. As stated above, this is a regular computer which applies the abstract steps of the judicial exception which pertain to performing a particle swarm algorithm computation. These are abstract steps, not additional information, and therefore the recited steps in the claim do not constitute a specific computer implemented technique.
Regarding the applicants assertion that the claims “improve a computer-implemented optimization process itself, for example, by "reducing premature convergence and optimizing execution of the particle swarm algorithm." The examiner believes this above claim step to be an improvement of the judicial exception itself (particle swarm algorithm). As stated above, improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Therefore, this method provides an improved method to conduct a particle swarm optimization algorithm, but does disclose an improvement in technology.
Conclusion: The examiner maintains the rejection.
2.Issue: Applicant argues that the invention as claimed provides a concrete solution to a technological problem and improves the functioning of a computing device during a computer-implemented algorithm, specifically via a runtime modification of the algorithm's operative behavior. Furthermore, the applicant argues that information generated and used by the claimed invention is not an end in itself, but rather is usable as an input to control operations, which changes algorithm execution. By example, the algorithm operates by controlling how information is shared and propagated among particles at Runtime which are then used to update particle movement.
Rule:MPEP 2106.05(a)(II) states “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology”
MPEP 2106.05(a)(I) states “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691”
Analysis: Regarding the assertion that claimed invention provides a concrete solution to a technical problem, the examiner disagrees. The claimed invention does not provide a concrete solution to a technical problem, rather, the claimed invention is directed towards the improvement of an abstract idea. The claimed invention seeks to reduce premature convergence of a particle swarm optimization algorithm. This is a mathematic issue that occurs. When a particle swarm algorithm prematurely converges, the mathematical answer to the problem is sub-optimal. Furthermore, regarding the assertion that the outputs of the algorithm “is not an end in itself but rather is usable as an input to control operations, which changes algorithm execution.” The examiner notes that this is simply a further application or use of the judicial exception/algorithm. The claimed invention does not claim an actual application for the algorithm but instead states that the outputs are used as inputs for further steps, which is an improvement to the algorithm itself, rather than to a technology.
Furthermore, regarding the assertion that the algorithm improves the functioning of a computing device during a computer-implemented algorithm, the examiner will determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. As outlined in the claim, the claimed inventions algorithm uses a computer as a tool to solve a complex mathematic equation (the particle swarm optimization algorithm). The claim does not recite specific data structures or ways in which the computers memory storage processing etc. capabilities are improved, but instead the claim outlines how the mathematic process for computing the answer to a PSO algorithm is improved (reducing premature convergence). Therefore, the examiner understands this claim to be directed to improving an abstract idea rather than computer technology.
Conclusion: The examiner maintains the 101 rejection.
3.Issue: In reference to the USPTO's current subject matter eligibility framework, as updated in the 2024
Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the applicant argues that the claimed invention reflects a specific improvement to the functioning of a computer or to another technical field under Step 2A, Prong Two analysis.
Rule: The 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artifical intelligence, section A “Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field” States “An improvement in the judicial exception itself is not an improvement in the technology.[70] For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) ( Stanford I ), the applicant claimed methods of resolving a haplotype phase involving steps of determining an inheritance state based on received allele data using a Hidden Markov Model. The applicant further claimed determining a haplotype phase based on the pedigree data, the earlier-calculated inheritance state, transition probability data, and population linkage disequilibrium data using a computer system.[71] The applicant argued that the claimed process was an improvement over prior processes because it “yields a greater number of haplotype phase predictions,” but the court found it was not “an improved technological process” and instead was an improved “mathematical process.” [72] The court explained that such claims were directed to an abstract idea because they describe “mathematically calculating alleles' haplotype phase,” like the “mathematical algorithms for performing calculations” in prior cases.[73] Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible.[74]”
Analysis: As outlined and discussed above, the claims are directed to an improvement in a mathematical process (solving a PSO algorithm better by reducing premature convergence). This is a better way to solve the above math problem, not a way in which a computers capabilities are improved. This is tangential to the above example, where the applicant’s invention “yields a more accurate PSO algorithm result” rather than an improvement to a technological process.
Conclusion: The examiner maintains the 101 rejection.
4.Issue: In reference to Ex Parte Desjardins et al., Appeals Review Panel of the Patent Trial and Appeal Board, Appeal 2024-000567 (Decision September 26, 2025) "eligibility determination should turn on whether 'the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.". Applicant submits that the combination of features set forth in the amended independent claims, including as identified above, regard improvements to technology and computer functionality and support a determination of eligible subject matter of the amended claims and of patentability
Rule: 822 F.3d at 1339. Moreover, because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determination should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336. “Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity."
Analysis: As stated by the examiner, the examiner believes the claimed invention is directed to an abstract idea rather than the improvement of a technology. The claimed invention recites a better method of performing a PSO algorithm (a math problem) to reduce premature convergence (to get a better answer). The claimed invention does not tie the performance of the algorithm to computer performance in a way which discloses a potential improvement. For example, the examiner finds par 133 which states “accordingly, reconfiguration blocks provide for dynamic allocations and reallocations of blocks to and from regions. In addition to scaling up or down a region based on computing power, other causes for rescaling can be based on communication needs (e.g., to provide for more or less broadcasting), processing speed, storage resources (e.g., the number of blocks for storage), cache resources, memory (e.g., DRAM) resources, non-volatile memory resources, or the like. The present disclosure supports machine learning and artificial intelligence resources for assigning blocks in conventional and emerging architectures. The processing capabilities shown and described herein provide for scaling up and down processing regions, dynamically and substantially in real-time during operation.” The examiner does not believe that the claims disclose this potential improvement to a technology where a machine learning algorithm automatically assigns blocks at run time to re-scale processing power as used by the computing device. (I.e: if computing power is low, then increase the weight of signals received by the particles, or increase connectivity to increase convergence) Instead, the claims are directed to reducing premature convergence, which is a mathematical problem that arises when the answer determined by the algorithm is not the global optimum. In other words, it can be said that the quickest particle swarm algorithm that uses the least computer resources would be one that has the greatest premature convergence, as the algorithm would end very quickly. Therefore, these claims are directed to determining the best mathematical solution, not an improvement to computer technology.
Conclusion: The examiner maintains the 101 rejection.
5.Issue: Applicant argues that since claims 2-10 and 12-23 depend on claims 24 and 25, that claims 2-10, and 12-23 are patentable for the same reasons.
Conclusion: The examiner maintains the 101 rejections. The examiner believes that the best way to promote patentability is to demonstrate how the invention can be used to improve technology like how outlined in the specifications 130-135. The claims should demonstrate the optimization of computing resources in response to determinations made in the claims. The examiner is open to conducting an interview to help explain the position further.
Response to 103 Rejection
Applicant argues against previous 103 rejection to claims 1-23 in reference to new claims 24 and 25.
1.Issue: Applicant argues that Engelbrecht does not teach or suggest Applicant's claimed features of "optimizing particle swarm algorithm" execution, including by reducing premature convergence. Engelbrecht does not teach or suggest Applicant's claimed, "information value," which is calculated from positional (e.g., "best particle position," a "best group position," and a "local best position") and non-positional data (e.g., a "percentage or amount of space explored" and a "number of previous iterations"). Further Engelbrecht does not teach or suggest those features in combination with "information value" determined and used to generate "shared information for information sharing," nor a determination being made that the "information value satisfies a threshold condition over a plurality of iterations" and, in response thereto, "information sharing" is modified. Moreover, Engelbrecht does not teach or suggest those features in combination with the modification associated with at least one of "a swarm-level topology selection," "at least one radius of connectivity," "a weight of a signal received from at least some of the plurality of particles," "information exchanged between particles," and "at least one particle group assignment." Still further, Applicant respectfully submits Engelbrecht does not teach or suggest those features in combination with propagating the "shared information" according to the "modified information sharing," updating particle "velocity or position," and iteratively recalculating the "information value," as recited in Applicant's independent claims 24 and 25.
Analysis and conclusion: The applicant arguments do not contain specific points as to why the prior art of Engelbrecht does not teach or suggest the above claim limitation. The examiner asks the applicant to refer to the 103 rejections for mappings. The rejection is maintained by the examiner.
2.Issue: Applicant argues that Engelbrecht regards a neural-guided particle swarm optimization approach in which each particle uses an artificial neural network to select which informant to follow and in which success of the neural network is updated based on whether following that guidance improves fitness. Unlike independent claims 24 and 25, Engelbrecht is materially different and does not teach or suggest the combination of features described above
Rule: MPEP 2141.01(a) states “When determining whether a prior art reference meets the "same field of endeavor" test for the analogous art, the primary focus is on what the reference discloses. Airbus, 41 F.3d at 1380. The examiner must consider the disclosure of each reference "in view of the ‘the reality of the circumstances.’" Airbus, 41 F.3d at 1380 (quoting Bigio, 381 F.3d at 1326, 72 USPQ2d at 1212). These circumstances are to be weighed "from the vantage point of the common sense likely to be exerted by one of ordinary skill in the art in assessing the scope of the endeavor." Airbus, 41 F.3d at 1380. See also Donner Technology, LLC v. Pro Stage Gear, LLC, 979 F.3d 1353, 2020 USPQ2d 11335 (Fed. Cir. 2020); Sanofi-Aventis, 66 F.4th at 1378; and Netflix, Inc. v. DivX, LLC, 80 F.4th 1352, 1358-59, 2023 USPQ2d 1057 (Fed. Cir. 2023) ("The field of endeavor is ‘not limited to the specific point of novelty, the narrowest possible conception of the field, or the particular focus within a given field.’") (quoting Unwired Planet, LLC v. Google Inc., 841 F.3d 995, 1001, 120 USPQ2d 1593, 1597 (Fed. Cir. 2016))”
Analysis: The prior art of Engelbrecht discusses particle swarm optimization algorithms. Under a common sense viewpoint, this is from the same field of endeavor as the claimed invention. As understood by the examine, the prior art of Engelbrecht is not a neural-guided particle swarm optimization approach as suggested by the applicant. The prior art of Engelbrecht is a textbook chapter which discusses the particle swarm optimization algorithm as well as multiple different variations of the algorithm as known in the prior art. While Engelbrecht does discuss applications of the PSO algorithm in training neural networks, the algorithms discussed in Engelbrecht are basic PSO algorithms which substantially contain the steps of the claimed invention. Engelbrecht does not teach away from what is claimed in the invention and therefore is applicable prior art which teaches the recited steps. Where it would be obvious to one ordinarily skilled in the art to follow a textbook algorithm implementation when solving a particle swarm optimization algorithm.
Conclusion: Rejection is maintained by the examiner, as the scope of the claims has been changed new prior art may be used for mapping.
3.Issue: Applicant argues that features of independent claims 24 and 25 are missing from the teachings of Engelbrecht as well as from the additionally cited prior arts of Benhalem and Santangeli and, accordingly, claims 24 and 25 cannot be obvious in view of those references under 35 USC § 103. And that since 4-10, 12 and 16-23 depend directly or indirectly from independent claims 24 or 25, respectively, that claims 4-10, 12 and 16-23 are also allowable over the cited prior art.
Analysis and conclusion: As the scope of the claims has been changed, new prior art may be used for mapping.
End Response to Arguments
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-3, 6-10, 14-25 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 24
Step 1: Is the claimed invention one of the four statutory categories?:
YES. The claim recites A computer-implemented method for reducing premature convergence and optimizing execution of a particle swarm algorithm, the method comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 24 recites:
receiving, by at least one computing device during an iteration of the particle swarm algorithm and from each of a plurality of particles exploring a design space, particle information representing at least one of a best particle position, a best group position, and a local best position,
The examiner interprets the broadest reasonable interpretation of receiving particle information to be an observation of particles in a swarm process to determine information relating to that particle. For example, a particle in a 2D space position value can be defined as a Euclidian distance from a global minimum. Among two particles, one of those particles will have the “best group position.” Under broadest reasonable interpretation, this claim limitation encompasses performing a Euclidian distance calculation of two particles to a global minimum in a 2D vector space, and then observing which position is best. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Further, The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Since this claim under broadest reasonable interpretation is an observation which can be performed with pen and paper, this claim recites an abstract idea.
and [receiving] additional information representing at least one of local exploration space characteristics, a number of previous iterations, a percentage or amount of space explored by at least some of the plurality of particles, a historical placement of a local best position, a historical local best position found by a subgroup, and a confidence score;
Under broadest reasonable interpretation, this claim encompasses receiving a number of previous iterations for a particle swarm process. This is an observation of how many passed iterations have occurred in a process, which is simply counting how many iterations have passed. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to an observation of how many iterations have passed, this claim recites an abstract idea.
determining, by the at least one computing device from the particle information and the additional information, an information value for at least one particle or subgroup;
This claim limitation is understood as determining an information value (a value) using the particle information (a positional value) and additional information (a value of number iterations or percentage of space explored). Under broadest reasonable interpretation, this claim can be interpreted as either a mathematic calculation, or a mental judgement. For instance, the past 10 iterations have seen no increase in fitness, so the information value will be large to promote higher convergence. Or, only 10% space has been explored, so the information value will promote a higher spread. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a judgement, the claim recites an abstract idea.
generating, by the at least one computing device using the information value, shared information for information sharing among at least some of the plurality of particles, the shared information comprising at least one of the information value, information representing a best particle position, information representing a best group position, information representing a local best position, and non-positional information;
This claim pertains to the generation of “shared information” which is information that will be shared with other particles comprising other information. For example, shared information may comprise the information value. This generation of shared information is labeling or determining which value is to be shared with other particles. This is an observation of values, and a judgement call be a user for what value should be shared information among a plurality of particles. For example, if the information value (a high number of iterations have passed, and the best particle position is high) share the best particle position as shared information. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “Since this limitation pertains to a judgement, the claim recites an abstract idea.
determining, by the at least one computing device, that the information value satisfies a threshold condition over a plurality of iterations;
This is a determination that a value satisfies a threshold criterion over a plurality of iterations. (in the past 5 iterations the information value has been greater than 5 units). This is an observation of numbers and an evaluation if it crosses a threshold. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “Since this limitation pertains to an evaluation, the claim recites an abstract idea.
in response to determining that the information value satisfies the threshold condition, modifying information sharing among at least some of the plurality of particles by at least one of: changing a swarm-level topology selection, changing at least one radius of connectivity, changing a weight of a signal received from at least some of the plurality of particles, changing specific information exchanged between particles, and changing at least one particle group assignment;
As stated above, determining that the information value satisfies the threshold condition is an evaluation by a user which is a mental process. The “modification” steps as outlined above are modifications to mathematic equations. For example, “changing a weight of a signal received from at least some of the plurality of particles” is understood to be changing a mathematic value which corresponds to a weight in the machine learning algorithm. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore, the claim recites an abstract idea.
propagating, by the at least one computing device, the shared information among at least some of the plurality of particles according to the modified information sharing; updating, by the at least one computing device, a velocity or position of at least one particle using the propagated shared information;
As the examiner understands, sharing/propagating in this context constitutes a modification of a particle’s parameters, i.e.: velocity, based on the information value received. Position in this context refers to the particles position equation which is modified by the velocity. Where Movement in this context is the modification of the position equation based on the velocity equation. These steps as outlined are calculations of math equations. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Since this claim limitation can be interpreted as determining the movement equation for a particle through a mathematic calculation, this claim limitation is directed to an abstract idea.
and iteratively recalculating, by the at least one computing device, the information value in a subsequent iteration of the particle swarm algorithm and iteratively updating at least one of the swarm-level topology selection, the at least one radius of connectivity, the weight of the signal, the specific information exchanged between particles, and the at least one particle group assignment based on the recalculated information value, thereby reducing premature convergence and optimizing execution of the particle swarm algorithm.
Recalculating in the claim is a recitation of a mathematic calculation to determine an information value after the steps of the claim have been performed until a solution to the algorithm has been determined. As stated previously, updating in this context refers to a change in mathematic parameters to a machine learning equation. Reducing premature convergence and optimizing execution of an algorithm refers to finding a better solution to an algorithm that more closely matches a global minimum/maximum. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the above steps which correlate to mathematic calculations are abstract processes.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 24 additionally recites receiving, by at least one computing device
This computing device performs the abstract ideas listed above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, this limitation does not integrate the exception into a practical application.
Step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, NO. As stated in Step 2A Prong 2, the additional elements in this claim are mere instructions to apply the exception. Therefore, these limitations in the claim do not provide significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 24 is not eligible under 35 USC 101.
Claim 25:
Claim 25 is an effective duplicate of claim 24 except that it depends on a computer implemented system which is a machine and is therefore rejected under a similar rational to claim 24. Furthermore, the additional limitations of “the system comprising: at least one computing device accessing instructions stored on non-processor readable media that, when executed by the at least one computing device, cause the at least one computing device for:” Is understood as generic computer machinery which performs the judicial exceptions outlined above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, this limitation does not integrate the exception into a practical application.
Claim 2
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 24 which is a process
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 2 recites: wherein the additional information is generated by machine learning using at least one of historical data and application-specific data, and
This limitation recites using machine learning to generate the additional information, which is “of local exploration space characteristics, a number of previous iterations, and a percentage or amount of space explored by at least some of the plurality of particles” The claim does not provide any detailed about how the machine learning model operates or how the generation is done, only that is uses historical and application-specific data. The recitation of “historical and application-specific” is a general claim limitation, since “historical and application-specific” applies to all fields. The broadest reasonable interpretation of using machine learning to generate additional information, like an amount of space explored, covers the performance of a mathematical calculation. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. Because this claim limitation pertains to a mathematic limitation, the claim recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 2 additionally recites further wherein affecting the execution of the at least one application includes providing at information in the form of an alert or a message.
The examiner believes affecting the execution of an application by “providing at information in the form of an alert or a message” to be an insignificant application of the judicial exception. One example of an Insignificant application to a judicial exception given in MPEP 2106.05(g) is “Printing or downloading generated menus.” This limitation is similar to the example above, as it can be understood as simply displaying the generated information. Therefore this limitation does not integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception?
NO. As stated in Step 2A Prong 2, The claim limitation is a form of insignificant application as discussed in the MPEP. Therefore this limitation does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 2 is not eligible under 35 USC 101.
Claim 3
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 2 which is a process
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 3 recites Wherein the machine learning is implemented by at least one neural network-based architecture.
The claim does not provide any details about how the neural network based architecture operates or how the neural network architecture is used to generate the additional information. The plain meaning of generating additional information encompasses a mental process or evaluation (i.e.: a user doing an optimization iteration by iteration keeping track of how many iterations pass.) MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore this claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 3 does not recite any additional elements that could integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. Claim 3 does not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 3 is not eligible under 35 USC 101.
Claim 6
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 24, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 6 recites using,
As the examiner understands under broadest reasonable interpretation, using the information value for a respective mode of operation for sharing the information value, encompasses sharing the information competitively or cooperatively. This means that the information is able to be shared among all the particles (cooperatively) or only shared among the members of a subswarm (competitively). As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may decide to use a cooperative mode of operation to exchange information to have a greater weight to improve consolidation rather than spread. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 6 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 6 is not eligible under 35 USC 101.
Claim 7
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 6, which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 7 recites wherein the respective mode of operation includes a collaborative mode of operation and a competitive mode of operation.
As the examiner understands under broadest reasonable interpretation, using the information value for a respective mode of operation for sharing the information value, encompasses sharing the information competitively or cooperatively. This means that the information is able to be shared among all the particles (cooperatively) or only shared among the members of a subswarm (competitively). As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may decide to use a cooperative mode of operation to exchange information to have a greater weight to improve consolidation rather than spread. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. The claim does not recite additional elements beyond what already discussed.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. The claim does not recite additional elements beyond what already discussed.
Based on the above facts, the office concludes that claim 7 is not eligible under 35 USC 101.
Claim 8:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 24, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 8 recites using,
Under broadest reasonable interpretation, the examiner understands using the information value to force groups to “disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. “ is to change how the information is shared among groups. For example, to assign a particle to a different subgroup is to state that the particles information is no longer shared with its previous group and is now shared with a new group. As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may move particles to consolidate them into larger groups. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 8 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 8 is not eligible under 35 USC 101.
Claim 9
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 24, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 9 recites adjusting,
Under broadest reasonable interpretation, the examiner understands using the information value to force groups to “disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. “ is to change how the information is shared among groups. For example, to assign a particle to a different subgroup is to state that the particles information is no longer shared with its previous group and is now shared with a new group. As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may move particles to consolidate them into larger groups. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 8 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 9 is not eligible under 35 USC 101.
Claim 10:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 9, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 10 recites ranking,
Under the broadest reasonable interpretation, the examiner understands ranking subgroups based on effectiveness to be a judgement performed by a user. For example, a user may judge the subgroup with the highest fitness scores to have a higher ranking. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation relates to a judgement (the mental process of ranking things) this claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 10 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 10 is not eligible under 35 USC 101.
Claim 14:
Claim 14 is an effective duplicate of claim 2 with the only difference being that it depends on claim 25. Due to the reasons discussed on claim 2 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 14 is not eligible under 35 USC 101.
Claim 15:
Claim 15 is an effective duplicate of claim 3 with the only difference being that it depends on claim 25. Due to the reasons discussed on claim 3 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 15 is not eligible under 35 USC 101.
Claim 16
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The system of claim 25 which is a machine
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
using the information value to adjust topological and operational characteristics during the iteration of the particle swarm optimization.
Using information value to adjust topological and operational characteristics during an iteration of the particle swarm algorithm under broadest reasonable interpretation encompasses modifying how particles in the swarm are connected as well as how they share information. With respect to claim 1, this would be the equivalent to observing an information value, and then changing how the information is shared between two particles as a result. For instance, an information value which suggests that a large portion of the space has been explored may lead to a judgement to change topology to increase neighborhood size as well as increase the social factor weights for velocity in the particle as an operational characteristic. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation under broadest reasonable interpretation is a judgement for how to change the particles characteristics, this limitation pertains to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 4 additionally recites wherein the at least one computing device is further configured by executing instructions stored on non-transitory processor readable media to perform steps including:
As stated under claim 24, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of claim 16, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 16 is not eligible under 35 USC 101.
Claim 17
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The system of claim 25, wherein determining the information value further comprises: which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 17 recites: calculating, by the at least one computing device:
a weight of a signal received from at least some of the plurality of particles;
at least one radius of connectivity;
a topology selection;
and a number of subgroups, groups, neighborhoods, clans or rings with which a respective one of the plurality of particles shares information.
This claim limitation is understood as calculating some information from the particle swarm algorithm during the determination step. This claim can be interpreted as a mathematic calculation. For instance, calculating a weight of a signal received is simply adding up the mathematic equations used in the particles that correlate to signal weight. calculating a radius of connectivity is an observation of what mathematic equation is used in determining neighborhood sizes (i.e.: Euclidian distance) and performing the calculation or measuring the largest distance between any two particles in a swarm. Calculating a topology selection as well as a number of subgroups to share information is a judgment on topology and information sharing based on a calculation. i.e. a large space has been explored, the topology will be modified to have more connections between particles. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a calculation and judgement, the claim recites an abstract idea. Since under broadest reasonable interpretation this claim falls under mental processes of observations and judgement as well as mathematic calculation, this claim pertains to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 17 does not recite additional elements that could integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. Claim 17 does not recite additional elements that could amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 17 is not eligible under 35 USC 101.
Claim 18
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The system of claim 17 which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 18 inherits the limitations of claim 17, and therefore is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 12 additionally recites wherein the topology section selection includes at least one of two connections per node and all nodes connected.
As the examiner understands, this claim limitation pertains to the swarm-level topology selection, where the selection includes having two connections per node or all nodes connected. See claim interpretation section. This limitation is insignificant activity. The MPEP 2106.05(g)(2) considers “Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).” When considering if a limitation is insignificant activity. A swarm level topology including a selection of connections per node or all connected nodes is nominally and tangentially related to the invention, as all particle swarm optimization algorithms contain swarm topologies with connections inherently. The MPEP 2106.05(g)(3) also considers “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” As stated previously, all particle swarm optimization algorithms contain swarm topologies with connections inherently, so this step of selecting connections per node is a necessary data gathering step. Because this limitation in the claim is insignificant activity, it does not integrate the exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The limitation above is insignificant activity. Under step 2B The MPEP 2106.05(g)(1) considers Whether the extra-solution limitation is well known. In “Topology Selection for Particle Swarm Optimization” by Liu et al. 2016, the researches state “The global best (gbest) topology and the local best (lbest) topology are two common social topologies in PSO. In the gbest topology, each particle is connected with all other particles (Examiner note: all connected nodes), i.e., it is a fully connected graph. In the lbest topology, each particle is only connected with its nearest K neighbors. In most publications and in this paper, K=2 is used (Examiner note: two connections per node) without loss of generality.” Since this limitation is common, it does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 18 is not eligible under 35 USC 101.
Claim 19:
Claim 19 is an effective duplicate of claim 6 with the only difference being that it depends on claim 25. Due to the reasons discussed on claim 6 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 19 is not eligible under 35 USC 101.
Claim 20:
Claim 20 is an effective duplicate of claim 7 with the only difference being that it depends on claim 25. Due to the reasons discussed on claim 7 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 20 is not eligible under 35 USC 101.
Claim 21:
Claim 21 is an effective duplicate of claim 8 with the only difference being that it depends on claim 25. Due to the reasons discussed on claim 8 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 21 is not eligible under 35 USC 101.
Claim 22:
Claim 22 is an effective duplicate of claim 9 with the only difference being that it depends on claim 25. Due to the reasons discussed in claim 9 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 22 is not eligible under 35 USC 101.
Claim 23:
Claim 23 is an effective duplicate of claim 10 with the only difference being that it depends on claim 25. Due to the reasons discussed in claim 1 and claim 25, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 23 is not eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 6-10, and 16-25 are rejected under 35 U.S.C. 103 as being unpatentable over Engelbrecht_2007 (Computational Intelligence: An Introduction, Second Edition A.P. Engelbrech. Chapter 16).
Claim 24:
EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization makes obvious A computer-implemented method for page 289 par 3: “Basic
variations of the PSO are described in Section 16.3, while more elaborate improvements are given in Section 16.5.”) execution of a particle swarm algorithm, the method comprising: (page 289 par 1: “The particle swarm optimization (PSO) algorithm is a population-based search algorithm based on the simulation of the social behavior of birds within a flock. The initial intent of the particle swarm concept was to graphically simulate the graceful and unpredictable choreography of a bird flock [449], with the aim of discovering patterns that govern the ability of birds to fly synchronously, and to suddenly change direction with a regrouping in an optimal formation. From this initial objective, the concept evolved into a simple and efficient optimization algorithm. “ …. page 296 par 4: “With reference to Algorithms 16.1 and 16.2, the optimization process is iterative. Repeated iterations of the algorithms are executed until a stopping condition is satisfied.”)
receiving, by at least one computing device during an iteration of the particle swarm algorithm and from each of a plurality of particles exploring a design space, (page 289 par 2: “In PSO, individuals, referred to as particles, are “flown” through hyperdimensional search space.”) particle information representing at least one of a best particle position, a best group position, and a local best position, (page 294 par 2: “The social component, c2r2(yˆ −xi), in the case of the gbest PSO or, c2r2(yˆi − xi), in the case of the lbest PSO, which quantifies the performance of particle i relative to a group of particles, or neighbors. Conceptually, the social component resembles a group norm or standard that individuals seek to attain. The effect of the social component is that each particle is also drawn towards the best position found by the particle’s neighborhood.”) Examiner note: Where it is inherent that particle information must be received when a particle is drawn towards a best position found by the particle’s neighborhood
and additional information representing at least one of local exploration space characteristics, a number of previous iterations, a percentage or amount of space explored by at least some of the plurality of particles, a historical placement of a local best position, a historical local best position found by a subgroup, and a confidence score; (page 298 par 6: “The following stopping conditions have been used: • Terminate when a maximum number of iterations, or FEs, has been exceeded.” (Examiner note: where it is inherent that in order for the algorithm to stop when a maximum number of iterations has been reached, that the number of previous iterations is being received by the computing device).
generating, by the at least one computing device using the information value (page 290 par 6: “The personal best position, yi, associated with particle i is the best position the particle has visited since the first-time step” Examiner note: This is using the additional information of a historical local best position, as well as particle information of best position found. See section 16.1.1 Global best PSO and equation 16.2. where this constitutes an information value. Where section 16.1 makes obvious information value but is made more explicit section 16.3 of the textbook below) , shared information for information sharing among at least some of the plurality of particles, the shared information comprising at least one of the information value, information representing a best particle position, information representing a best group position, information representing a local best position, and non-positional information; (page 290 par 4 :” For the global best PSO, or gbest PSO, the neighborhood for each particle is the entire swarm. The social network employed by the gbest PSO reflects the star topology (refer to Section 16.2). For the star neighborhood topology, the social component of the particle velocity update reflects information obtained from all the particles in the swarm. In this case, the social information is the best position found by the swarm, referred to as ˆy(t).”)
EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization Does not expressly recite reducing premature convergence
determining, by the at least one computing device from the particle information and the additional information, an information value for at least one particle or subgroup;
determining, by the at least one computing device, that the information value satisfies a threshold condition over a plurality of iterations;
propagating, by the at least one computing device, the shared information among at least some of the plurality of particles according to the modified information sharing;
updating, by the at least one computing device, a velocity or position of at least one particle using the propagated shared information;
and iteratively recalculating, by the at least one computing device, the information value in a subsequent iteration of the particle swarm algorithm and iteratively updating at least one of the swarm-level topology selection, the at least one radius of connectivity, the weight of the signal, the specific information exchanged between particles, and the at least one particle group assignment based on the recalculated information value, thereby reducing premature convergence and optimizing execution of the particle swarm algorithm.
EngelBrecht_2007 in section 16.3 Basic Variations, however, makes obvious determining, by the at least one computing device from the particle information and the additional information, an information value for at least one particle or subgroup; ; page 306 par 4: “The inertia weight, w, controls the momentum of the particle by weighing the contribution of the previous velocity – basically controlling how much memory of the previous flight direction will influence the new velocity. For the gbest PSO, the velocity equation changes from equation (16.2) to vij(t + 1) = wvij(t) + c1r1j(t)[yij(t) − xij(t)] + c2r2j(t)[ˆyj(t) − xij(t)” … page 307 par 5: “Linear decreasing, where an initially large inertia weight (usually 0.9) is linearly decreased to a small value (usually 0.4). From Naka et al. [619], Ratnaweera et al. [706], Suganthan [820], Yoshida et al. [941] w(t) = (w(0) − w(nt)) (nt − t) nt + w(nt) (16.26) where nt is the maximum number of time steps for which the algorithm is executed, w(0) is the initial inertia weight, w(nt) is the final inertia weight, and w(t) is the inertia at time step t. Note that w(0) > w(nt).” ) Examiner note: Where the information value is particle velocity. This velocity is determined by the particle information (moves towards gbest) and is determined by the additional information (the time step) which influences the inertia weight component of the velocity.)
EngelBrecht_2007 in section 16.3 Basic Variations and EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 in section 16.3 Basic Variations and EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization
The rationale for doing so would have been to follow a motivation proposed in the art. EngelBrecht_2007 in section 16.3 Basic Variations states tin par 306 par 5: “The inertia weight was introduced by Shi and Eberhart [780] as a mechanism to control the exploration and exploitation abilities of the swarm, and as a mechanism to eliminate the need for velocity clamping [227]. The inertia weight was successful in addressing the first objective, but could not completely eliminate the need for velocity clamping. The inertia weight, w, controls the momentum of the particle by weighing the contribution of the previous velocity – basically controlling how much memory of the previous flight direction will influence the new velocity. “ In order to take advantage of the benefit to control exploration and exploitation abilities of a swarm, the user of EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization would be inclined to add additional information in the form of number of iterations, to control the velocity using inertia. Therefore, it would have been obvious to combine the velocity equation of EngelBrecht_2007 in section 16.3 Basic Variations with the PSO algorithm of EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization for the benefit of controlling exploration and exploitation to obtain the invention as specified in the claims.
Engelbrecht_2007 section 16.5 Single-Solution Particle Swarm Optimization, however, makes obvious reducing premature convergence (page 323 par 3: “Particles are randomly selected as parents, not on the basis of their fitness. This prevents the best particles from dominating the breeding process. If the best particles were allowed to dominate, the diversity of the swarm would decrease significantly, causing premature convergence. It was found empirically that a low breeding probability of 0.2 provides good results [536].”
determining, by the at least one computing device, that the information value satisfies a threshold condition over a plurality of iterations; in response to determining that the information value satisfies the threshold condition, modifying information sharing among at least some of the plurality of particles by at least one of: changing a swarm-level topology selection, changing at least one radius of connectivity, changing a weight of a signal received from at least some of the plurality of particles, changing specific information exchanged between particles, and changing at least one particle group assignment;
page 326 par 10 - 327 par 1: “Particles are randomly assigned to one of the sub-swarms. Each sub-swarm can be in one of two phases:
• Attraction phase, where the particles of the corresponding sub-swarm are
allowed to move towards the global best position.
• Repulsion phase, where the particles of the corresponding sub-swarm move
away from the global best position.
For their multi-phase PSO (MPPSO), the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration coefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position. Sub-swarms switch phases (Examiner note: Changing particle group assignment) either• when the number of iterations in the current phase exceeds a user specified threshold, or” when particles in any phase show no improvement in fitness during a userspecified number of consecutive iterations.) Examiner note: a threshold condition over a plurality of iterations.
propagating, by the at least one computing device, the shared information among at least some of the plurality of particles according to the modified information sharing; page 326 par 10 - 327 par 1: “Particles are randomly assigned to one of the sub-swarms. Each sub-swarm can be in one of two phases:
• Attraction phase, where the particles of the corresponding sub-swarm are
allowed to move towards the global best position.
• Repulsion phase, where the particles of the corresponding sub-swarm move
away from the global best position.
For their multi-phase PSO (MPPSO), the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration coefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position. Sub-swarms switch phases Examiner note : based on what phase the particle is in, the global best position (shared information value) is propagated differently, causing the velocity update to be different.
updating, by the at least one computing device, a velocity or position of at least one particle using the propagated shared information; (page 327 par 1: “the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration coefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position”)
and iteratively recalculating, by the at least one computing device, the information value in a subsequent iteration of the particle swarm algorithm and iteratively updating at least one of the swarm-level topology selection, the at least one radius of connectivity, the weight of the signal, the specific information exchanged between particles, and the at least one particle group assignment based on the recalculated information value, thereby reducing premature convergence and optimizing execution of the particle swarm algorithm. Page 318 par 3:” To combine the advantages of better exploration by neighborhood structures and the faster convergence of highly connected networks, Suganthan combined the two approaches [820]. The search is initialized with an lbest PSO with
nN = 2 (i.e. with the smallest neighborhoods). The neighborhood sizes are then increased with increase in iteration until each neighborhood contains the entire swarm (i.e. nN = ns). Growing neighborhoods are obtained by adding particle position xi2 (t) to the neighborhood of particle position xi1 (t) if
||xi1 (t) − xi2 (t)||2 dmax < where dmax is the largest distance between any two particles, and with nt the maximum number of iterations. This allows the search to explore more in the first iterations of the search, with faster convergence in the later stages of the search.” (Examiner note: Where the particle information is the best particle position, and the additional information is the number of iterations, which makes up the information value. Where the particles group assignment to a group is changed based on the information value. And furthermore where “To combine the advantages of better exploration by neighborhood structures and the faster convergence of highly connected networks” is understood to be reducing premature convergence and optimizing execution of the particle swarm algorithm.
EngelBrecht_2007 sections 16.1, 16.3, and 16.5 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 section 16.5 and 16.1. The rationale for doing so would have been to follow a teaching proposed in EngelBrecht_2007 section 16.5 page 315: “A variety of PSO variations have been developed, mainly to improve the accuracy of solutions, diversity and convergence behavior. This section reviews some of these variations for locating a single solution to unconstrained, single-objective, static optimization
problems.” Therefore, it would have been obvious to combine EngelBrecht_2007 section 16.5 and 16.1 for the benefit of locating single solutions in optimizations problems more efficiently to obtain the invention as specified in the claims.
Claim 25:
Claim 25 is effectively similar to claim 24 and is therefore rejected under a similar rational. Additionally, EngelBrecht_2007 makes obvious the additional limitations of the system comprising: at least one computing device accessing instructions stored on non-processor readable media that, when executed by the at least one computing device, cause the at least one computing device for: (Title: “Computational Intelligence” … page 357 par 1: “algorithm, summarized in Algorithm 16.17, a swarm of particles is randomly created, where each particle represents a single NN. Each NN plays in a tournament against a group of randomly selected opponents, selected from a competition pool (usually consisting of all the current particles of the swarm and all personal best positions). After each NN has played against a group of opponents, it is assigned a score based on the number of wins, losses and draws achieved. These scores are then used to determine personal best and neighborhood best solutions. Weights are adjusted using the position and velocity updates of any PSO algorithm.”) Examiner note: Where this implies an makes obvious to one ordinarily skilled in the art that this algorithm is being performed on a computing device where computing devices perform functions stored on non-transitory processors. )
Claim 6:
The method of method of claim 24, further comprising:
EngelBrecht_2007 section 16.5 however makes obvious using, by the at least one computing device, the information value page 327 par 1: “For their multi-phase PSO (MPPSO), the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration coefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position. Sub-swarms switch phases either• when the number of iterations in the current phase exceeds a user specified threshold, or”) for a respective mode of operation for sharing the information value. Page 326 under 16.5.4 Sub-Swarm based PSO par 1: “A number of cooperative and competitive PSO implementations that make use of multiple swarms have been developed. Some of these are described below”
Claim 7:
The method of claim 6,
EngelBrecht_2007 section 16.5 makes obvious wherein the respective mode of operation includes a collaborative mode of operation and a competitive mode of operation.
Page 326 under 16.5.4 Sub-Swarm based PSO par 1: “A number of cooperative and competitive PSO implementations that make use of multiple swarms have been developed. Some of these are described below”
Claim 8:
The method of claim 24, further comprising: EngelBrecht_2007 section 16.5 makes obvious using, by the at least one computing device, the information value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.”) to force some subgroups to disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. (page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. It can also happen that individuals may migrate between groups.”)
Claim 9:
The method of claim 24, further comprising:EngelBrecht_2007 section 16.5 makes obvious adjusting, by the at least one computing device as a function of the information value, value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.”) at least one subgroup of the plurality of particles to disperse, randomize, or be assigned to at least one different subgroup. (page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. It can also happen that individuals may migrate between groups.”)
Claim 10:The method of claim 9, further comprising:
EngelBrecht_2007 section 16.5 makes obvious ranking, by the at least one computing device, the at least one subgroup of the plurality of particles based on the at least one subgroup's effectiveness.
Page 321 algorithm 16.5: “Calculate the fitness of all particles; for each particle i = 1,...,ns do Randomly select nts particles; Score the performance of particle i against the nts randomly selected particles; end Sort the swarm based on performance scores; Replace the worst half of the swarm with the top half, without changing the personal best positions”
Claim 16:
The system of claim 25 wherein the at least one computing device is further configured by executing instructions stored on non-transitory processor readable media to perform steps including:
EngelBrecht_2007 section 16.5 Single-Solution Particle Swarm Optimization, however, makes obvious using, by the at least one computing device, the information value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.” to adjust topological and operational characteristics during the iteration of the particle swarm algorithm. ( page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. (Examiner note: an adjustment of operational characteristics) It can also happen that individuals may migrate between groups.” (Examiner note: an adjustment of topological characteristics)
Examiner note: where the combination of group interaction with the environment and selection of global best is the information value used in this implementation of PSO
EngelBrecht_2007 sections 16.1, and 16.5, are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 section 16.5 and 16.1. The rationale for doing so would have been to follow a teaching proposed in EngelBrecht_2007 section 16.5 page 315: “A variety of PSO variations have been developed, mainly to improve the accuracy of solutions, diversity and convergence behavior. This section reviews some of these variations for locating a single solution to unconstrained, single-objective, static optimization
problems.” Therefore, it would have been obvious to combine EngelBrecht_2007 section 16.5 and 16.1 for the benefit of locating single solutions in optimizations problems more efficiently to obtain the invention as specified in the claims.
Claim 17:
The system of claim 25, wherein determining the information value further comprises:
calculating, by the at least one computing device:
EngelBrecht_2007 section 16.1 makes obvious and a number of subgroups, groups, neighborhoods, clans or rings with which a respective one of the plurality of particles shares information.
Page 292 par 4: “Selection of neighborhoods is done based on particle indices. However, strategies have been developed where neighborhoods are formed based on spatial similarity (refer to Section 16.2). There are mainly two reasons why neighborhoods based on particle indices are preferred: 1. It is computationally inexpensive, since spatial ordering of particles is not required. For approaches where the distance between particles is used to form neighborhoods, it is necessary to calculate the Euclidean distance between all pairs of particles, which is of O(n2 s) complexity. 2. It helps to promote the spread of information regarding good solutions to all particles, irrespective of their current location in the search space. It should also be noted that neighborhoods overlap. A particle takes part as a member of a number of neighborhoods. This interconnection of neighborhoods also facilitates the sharing of information among neighborhoods, and ensures that the swarm converges on a single point, namely the global best particle. The gbest PSO is a special case of the lbest PSO with nNi = ns.”
EngelBrecht_2007 section 16.2 makes obvious a topology selection;
par 301: “For sparsely connected networks with a large amount of clustering in neighborhoods, it can also happen that the search space is not covered sufficiently to obtain the best possible solutions. Each cluster contains individuals in a tight neighborhood covering only a part of the search space. Within these network structures there usually exist a few clusters, with a low connectivity between clusters. Consequently information on only a limited part of the search space is shared with a slow flow of information between clusters. Different social network structures have been developed for PSO and empirically studied.” Examiner note: Where a selection of different social networks is a topology selection.
EngelBrecht_2007 section 16.2 makes obvious at least one radius of connectivity;
par 317 par 5: “Neighborhoods are usually formed on the basis of particle indices. That is, assuming a ring social network, the immediate neighbors of a particle with index i are particles with indices (i − 1 mod ns) and (i − 1 mod ns), where ns is the total number of particles in the swarm. Suganthan proposed that neighborhoods be formed on the basis of the Euclidean distance between particles [820]. For neighborhoods of size nN , the neighborhood of particle i is defined to consist of the nN particles closest to particle i. Algorithm 16.4 summarizes the spatial neighborhood selection process.” Examiner note: Where the Euclidian distance from the last particle forms the last radius of connectivity.
Where it would be obvious for one ordinarily skilled in the art to combine EngelBrecht_2007 sections 16.1 and 16.2. The rationale for doing so would have been to follow a teaching in the art. EngelBrecht_2007 section 16.2 in page 301 states “Each particle therefore imitates the overall best solution. The first implementation of the PSO used a star network structure, with the resulting algorithm generally being referred to as the gbest PSO. The gbest PSO has been shown to converge faster than other network structures, but with a susceptibility to be trapped in local minima. The gbest PSO performs best for unimodal problems.” … “Since information flows at a slower rate through the social network, convergence is slower, but larger parts of the search space are covered compared to the star structure. This behavior allows the ring structure to provide better performance in terms of the quality of solutions found for multi-modal problems than the star structure. The resulting PSO algorithm is generally referred to as the lbest PSO.”
Therefore, it would have been obvious to combine the algorithms and neighborhood dynamics of EngelBrecht_2007 section 16.1 and 16.5 with the presence of different topology selections of EngelBrecht_2007 section 16.2 for the benefit of solving either unimodal or multimodal problems more efficiently to obtain the invention as specified in the claims.
EngelBrecht_2007 sections 16.2, 16.5, and 16.1 do not expressly recite a weight of a signal received from at least some of the plurality of particles;
EngelBrecht_2007 section 16.3 however makes obvious a weight of a signal received from at least some of the plurality of particles; page 308: “Clerc proposes an adaptive inertia weight approach where the amount of change in the inertia value is proportional to the relative improvement of the swarm [134]. The inertia weight is adjusted according to wi(t + 1) = w(0) + (w(nt) − w(0)) emi(t) − 1 emi(t) + 1 (16.29) where the relative improvement, mi, is estimated as mi(t) = f(yˆi(t)) − f(xi(t)) f(yˆi(t)) + f(xi(t)) (16.30) with w(nt) ≈ 0.5 and w(0) < 1. Using this approach, which was developed for velocity updates without the cognitive component, each particle has its own inertia weight based on its distance from the local best (or neighborhood best) position. The local best position, yˆi(t) can just as well be replaced with the global best position yˆ(t). Clerc motivates his approach by considering that the more an individual improves upon his/her neighbors, the more he/she follows his/her own way, and vice versa. Clerc reported that this approach results in fewer iterations [134]” Examiner note: Where the inertia Is adjusted in proportion to distance from a local best position (ie weight of a signal from other particles)
EngelBrecht_2007 sections 16.1, 16.2, 16.3, and 16.5 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 sections 16.1, 16.2, 16.3, and 16.5The rationale for doing so would have been to follow a teaching as proposed in the art. EngelBrecht_2007 section 16.3 states page 307 par 1: “These approaches usually start with large inertia values, which decreases over time to smaller values. In doing so, particles are allowed to explore in the initial search steps, while favoring exploitation as time increases.” Therefore, it would have been obvious to combine the PSO implementations and applications of EngelBrecht_2007 sections 16.1, 16.2, and 16.5 with an adaptive signal of section 16.3 for the benefit of balancing exploration and exploitation to obtain the invention as specified in the claims.
Claim 18:
EngelBrecht_2007 section 16.2 makes obvious The system of claim 17, wherein the topology selection includes at least one of two connections per node (Page 301: “Different social network structures have been developed for PSO and empirically studied.” … “The ring social structure, where each particle communicates with its nN immediate neighbors. In the case of nN = 2, a particle communicates with its immediately adjacent neighbors as illustrated in Figure 16.4(b). Each particle attempts to imitate its best neighbor by moving closer to the best solution found within the neighborhood.) and all nodes connected. (page 301: “The star social structure, where all particles are interconnected as illustrated in Figure 16.4(a). Each particle can therefore communicate with every other particle. In this case each particle is attracted towards the best solution found by the entire swarm.)
Claim 19:
The limitations of claim 19 are substantially the same as those of claim 6 except that it depends from claim 25 and are therefore rejected due to the same reasons as outlined above for claim 6 and claim 25.
Claim 20:
The limitations of claim 20 are substantially the same as those of claim 7 except that it depends from claim 25 and are therefore rejected due to the same reasons as outlined above for claim 7 and claim 25.
Claim 21:
The limitations of claim 21 are substantially the same as those of claim 8 except that it depends from claim 25 and are therefore rejected due to the same reasons as outlined above for claim 8 and claim 25.
Claim 22:
The limitations of claim 22 are substantially the same as those of claim 9 except that it depends from claim 25 and are therefore rejected due to the same reasons as outlined above for claim 9 and claim 25.
Claim 23:
The limitations of claim 23 are substantially the same as those of claim 10 except that it depends from claim 25 and are therefore rejected due to the same reasons as outlined above for claim 10 and claim 25.
Claims 2-3, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Engelbrecht_2007 and further in view of Neural-Guided Particle Swarm Optimization by Benhalem and Lones (Benhalem_2020) and US 20220381433 A1 (Santangeli_2022)
Engelbrecht_2007 recites The method of claim 24, wherein the additional information (See claim 24)
further wherein affecting the execution of the at least one application (see claim 24)
However Engelbrecht_2007 does not explicitly recite:
generated by machine learning using at least one of historical data and application-specific data, and includes providing information in the form of an alert or a message.
Benhalem_2020, however, makes obvious is generated by machine learning (page 3 col 1 par 3: “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm 1. ANN-PSO is outlined in Algorithm 2, with highlighting showing the parts of the algorithm that differ from Algorithm 1. A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer. The number of input neurons is the same as the dimensionality of the problem, and it has one output neuron. At each iteration, each informant’s pbest is used as an input to the ANN, generating a response value from the output neuron (lines 19-23 in Algorithm 2). The pbest with the highest response value is then selected as the gbest used in Equation 1”) using at least one of historical data and application-specific data, and (page 3 col 2 par 1: “Within this evolutionary process, the fitness of an ANN is measured by its cumulative success in guiding particles to better locations, in a manner akin to reinforcement learning.”) Examiner note: Where this process as described is the use of historical data as outlined in the specification. Where a neural-guided PSO algorithm is interpreted as machine learning.
Engelbrecht_2007 and Benhalem_2020 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007 and Benhalem_2020.
The rationale for doing so would have been to follow a motivation proposed in the prior art by Benhalem_2020. Benhalem_2020 page 3 col 2 par 4 states “From these tables, it is evident that PSO-ANN performs better than PSO. The best mean result (shown underlined) is produced by ANN-PSO for every function in this test suite, and this advantage is retained as the dimensionality of the problem increases. This suggests (for these functions at least), that using an ANN to guide PSO is beneficial. For most of the problems, this benefit appears to be quite sizeable.” Benhalem_2020 page 3 col 1 par 4 also states “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm.” As stated, this neural guided PSO builds upon a standard PSO and provides a sizeable benefit to the algorithm. Engelbrecht_2007 discusses using a standard PSO algorithm and ways to optimize such algorithm, such as an inclusion of additional information. Benhalem_2020 does not explicitly teach the inclusion of additional information, but teaches page 3 col 1 par 4: “A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer.” One reasonably skilled in the art would know that a simple perceptron can be applied to other information in the particle swarm application such as additional information. An individual reasonably skilled in the art would have known that using the Neural guided PSO on top of the standard PSO algorithm of Engelbrecht_2007 with additional information would have produced such benefit with a reasonable expectation of success.
Therefore, it would have been obvious to combine the neural guided PSO of Benhalem_2020 with the standard PSO algorithms of Engelbrcht_2007 for the benefit of better performance to obtain the invention as specified in the claims.
Engelbrecht_2007 and Benhalem_2020 do not expressly recite includes providing at information in the form of an alert or a message.
Santangeli_2022, however, makes obvious includes providing at information in the form of an alert or a message. (par 34: “In many implementations, the burner system provides machine learning and optimization of prioritized burner performance, which may be defined by one or more criteria, for example, efficiency, emissions, etc. The system may maintain a history or log of optimized biases and may alert the system operator to trend deviations. The system may notify operators of equipment problems, such as drifting sensor calibrations, off-specification fuel, component wear, malfunction, and/or failure, and the like. The AI may use one or more multivariate analysis tools including learning models and/or particle swarm optimization. Such tools may be used to continually monitor and/or tune performance. The AI may enable progressive improvement and/or reprioritization of performance criteria.”)
Engelbrecht_2007, Benhalem_2020, and Santangeli_2022 are analogous art to the claimed invention because they are from the same field of endeavor called artificial intelligence. Engelbrecht_2007, and Benhalem_2020 are academic sources which describe a particle swarm optimization process and its permutations. Santengeli_2022 applies artificial intelligence (including a particle swarm process) to a burner system. See par 34: “The AI may use one or more multivariate analysis tools including learning models and/or particle swarm optimization.”
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007, Benhalem_2020, and Santangeli_2022.
The rationale for doing so would have been to apply a known technique to a known device ready for improvement to yield a predictable result. This application teaches applying the PSO algorithm as outlined to the execution of some application. Santangeli_2022 teaches the application of a PSO algorithm to a burner system application. Santangeli_2022 also teaches using this system to send alerts to a user. The prior art of Engelbrecht_2007 and Benhalem_2020 contain known particle swarm optimization techniques to improve the application device of Santangeli_2022. One ordinarily skilled in the art would know to apply known techniques of improving PSO algorithms to an application which uses a PSO algorithm. Therefore, it would have been obvious to combine the alert system of Santangeli_2022 with the PSO algorithm improvements of Engelbrecht_2007 and Benhalem_2020 for the benefit of containing a more efficient algorithm system, as well as gaining the benefit of notifying a user of issues in an application to obtain the invention as specified in the claims.
Claim 3:
Engelbrecht_2007 does not expressly recite The method of claim 2, wherein the machine learning is implemented by at least one neural network-based architecture.
Benhalem_2020, however, makes obvious The method of claim 2, wherein the machine learning is implemented by at least one neural network-based architecture. (page 3 col 1 par 3: “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm 1. ANN-PSO is outlined in Algorithm 2, with highlighting showing the parts of the algorithm that differ from Algorithm 1. A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer. The number of input neurons is the same as the dimensionality of the problem, and it has one output neuron. At each iteration, each informant’s pbest is used as an input to the ANN, generating a response value from the output neuron (lines 19-23 in Algorithm 2). The pbest with the highest response value is then selected as the gbest used in Equation 1”)
Engelbrecht_2007 and Benhalem_2020 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007 and Benhalem_2020.
The rationale for doing so would have been to follow a motivation proposed in the prior art by Benhalem_2020. Benhalem_2020 page 3 col 2 par 4 states “From these tables, it is evident that PSO-ANN performs better than PSO. The best mean result (shown underlined) is produced by ANN-PSO for every function in this test suite, and this advantage is retained as the dimensionality of the problem increases. This suggests (for these functions at least), that using an ANN to guide PSO is beneficial. For most of the problems, this benefit appears to be quite sizeable.” Benhalem_2020 page 3 col 1 par 4 also states “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm.” As stated, this neural guided PSO builds upon a standard PSO and provides a sizeable benefit to the algorithm. Engelbrecht_2007 discusses using a standard PSO algorithm and ways to optimize such algorithm, such as an inclusion of additional information. Benhalem_2020 does not explicitly teach the inclusion of additional information, but teaches page 3 col 1 par 4: “A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer.” One reasonably skilled in the art would know that a simple perceptron can be applied to other information in the particle swarm application such as additional information. An individual reasonably skilled in the art would have known that using the Neural guided PSO on top of the standard PSO algorithm of Engelbrecht_2007 with additional information would have produced such benefit with a reasonable expectation of success.
Therefore, it would have been obvious to combine the neural guided PSO of Benhalem_2020 with the standard PSO algorithms of Engelbrcht_2007 for the benefit of better performance to obtain the invention as specified in the claims.
Claim 14
The limitations of claim 14 are substantially the same as those of claim 2 except that it depends on claim 25 and therefore is rejected due to the same reasons as outlined above for claims 2 and 25.
Claim 15:
The limitations of claim 15 are substantially the same as those of claim 3 except that it depends on claim 25 and therefore is rejected due to the same reasons as outlined above for claims 3 and 25.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li_2014 (“Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems”)
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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